Exploratory Analysis for Large and Complex Problems Using SAS Enterprise Miner

This course is intended for analysts working with virtually any type of exploratory data analysis problem. Discovery in a complicated data set is one of the analyst's toughest problems. The course covers this discovery process using many real-world problems. There is a focus on fraud detection, with the recognition that the core principles of modeling to solve fraud detection are the basis of all exploratory data analysis. Analytical methods used in the course include decision trees, logistic regression, neural networks, link analysis, and social network analysis. In addition, analysts receive practical advice on presenting complex findings to their audience.

Learn how to

analyze in multiple dimensions

escape the limits of common methods

explore your most complex problems

successfully present findings to your audience

find rare events

find hidden relationships

reach deep into your data and find what others cannot.

Who should attend

Data analysts (market researchers, fraud researchers, and sales analysts); expert modelers or those who want to become expert; and the creative and curious

This class is taught in SAS Enterprise Miner and foundation SAS. Familiarity with SAS Enterprise Miner at the level presented in the Applied Analytics Using SAS Enterprise Miner course is helpful. Most of the techniques shown in this course using SAS Enterprise Miner are supplemented with similar approaches in foundation SAS.

This course addresses SAS Enterprise Miner software.

Predictive Analytics and Exploratory Data Mining

the relationship between fraud detection and exploratory data mining

the role of graphics in exploratory analysis

complexity in a 'PowerPoint world'

the analyst's dilemma

Working with Unstructured Data

data streams versus structured data

social network analysis as a solution to unstructured problems

statistical mechanics of network analyses

predicting with a network

complex networks versus reductionism

fraud detection with social network analysis

Exploratory Data Mining and Predictive Models

exploratory data mining success

predictive modeling methods

logistic regression

decision trees

neural networks

the truth about neural networks

comparing and contrasting predictive modeling methods

model structure and impact on exploratory results

graphical review of model results

Complex Exploratory Modeling

initial data screening

developing complex predictive models for exploratory efforts

identifying important variables

analyzing variables, domains, and clusters

graphical review of models and data

applying a complex predictive model to fraud detection

Exploratory Findings

extracting new hypotheses (exploratory findings) from the predictive model

building confidence with the exploratory findings

recognizing and overcoming impediments to acceptance by the target audience